A Grid Agent Model for Information Retrieval Based on Topic Map and Knowledge Elements Mining

Knowledge-based and artificial-intelligence-based inductive learning techniques can provide great opportunities to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. This paper introduces a new approach to knowledge-based information retrieval. It presents a grid agent model including three kinds of agents: Grid service agent, service request agent and grid space manager. Through actions of registry, discovery and access in grid information service discovery process, the grid space manager will find out the suitable services. Then aiming to implement the flexibility and intelligence of the grid agent, we think that every grid agent should remember the success and quality of the former cases by knowledge element mining and topic map building and reasoning, so that the agent can gain experience of userpsilas preference. The methods through which to mine knowledge elements from Web page resourcespsila and to build extended topic maps containing a knowledge elements level is also emphasized.